Publications

We consider the following problem: two nodes want to reliably communicate in a dynamic multihop network where some nodes have been compromised, and may have a totally arbitrary and unpredictable behavior. These nodes are called Byzantine. We consider the two cases where cryptography is available and not available.
We prove the necessary and sufficient condition (that is, the weakest possible condition) to ensure reliable communication in this context. Our proof is constructive, as we provide Byzantine-resilient algorithms for reliable communication that are optimal with respect to our impossibility results.
In a second part, we investigate the impact of our conditions in three case studies: participants interacting in a conference, robots moving on a grid and agents in the subway. Our simulations indicate a clear benefit of using our algorithms for reliable communication in those contexts

Multicast is the obvious choice for disseminating popular data on cellular networks. In spite of having better spectral efficiency than unicast, its performance is bounded by the user with the worst channel in the cell. To overcome this limitation, we propose to combine multicast with device-to-device (D2D) communications over an orthogonal channel. Such a strategy improves the efficiency of the dissemination process while saving resources at the base station. It is quite challenging, however, to decide which users should be served through multicast transmissions and which ones should receive the content via D2D communications. The progress of content dissemination through D2D communications depends on how users meet while on the move. The optimal decision for each content depends both on the status of the LTE channel (when the multicast transmission is executed) and on the evolution of the mobility process of the nodes from there on. We propose a learning solution based on a multi-armed bandit algorithm that dynamically selects the best allocation of users between multicast and D2D to guarantee the timely delivery of data. Numerical evaluations are performed to compare our proposal with the state-of-the-art scheme and an optimal but unfeasible strategy. We confirm that a proper mix of multicast and D2D helps operators save resources at the base station and that the learning algorithm can autonomously find a near-optimal configuration in a reasonable time.

We consider the following problem: two nodes want to reliably communicate in a dynamic multihop network where some nodes have been compromised, and may have a totally arbitrary and unpredictable behavior. These nodes are called Byzantine. We consider the two cases where cryptography is available and not available.
We prove the necessary and sufficient condition (that is, the weakest possible condition) to ensure reliable communication in this context. Our proof is constructive, as we provide Byzantine-resilient algorithms for reliable communication that are optimal with respect to our impossibility results.
In a second part, we investigate the impact of our conditions in three case studies: participants interacting in a conference, robots moving on a grid and agents in the subway. Our simulations indicate a clear benefit of using our algorithms for reliable communication in those contexts.

Pervasive networks formed by users’ mobile devices have the potential to exploit a rich set of distributed service components that can be composed to provide each user with a multitude of application level services. However, in many challenging scenarios, opportunistic networking techniques are required to enable communication as devices suffer from intermittent connectivity, disconnections and partitions. This poses novel challenges to service composition techniques. While several works have discussed middleware and architectures for service composition in well-connected wired networks and in stable MANET environments, the underlying mechanism for selecting and forwarding service requests in the significantly challenging networking environment of opportunistic networks has not been entirely addressed. The problem comprises three stages: i) selecting an appropriate service sequence set out of available services to obtain the required application level service; ii) routing results of a previous stage in the composition to the next one through a multi-hop opportunistic path; and iii) routing final service outcomes back to the requester. The proposed algorithm derives efficiency and effectiveness by taking into account the estimated load at service providers and expected time to opportunistically route information between devices. Based on this information the algorithm estimates the best composition to obtain a required service. It is shown that using only local knowledge collected in a distributed manner, performance close to a real-time centralized system can be achieved. Applicability and performance guarantee of the service composition algorithm in a range of mobility characteristics are established through extensive simulations on real/synthetic traces.

Achieving efficient content dissemination in mobile opportunistic networks becomes a big challenge when content sizes are large and require more capacity than what contact opportunities between nodes may offer. Content fragmentation solves only part of the problem, as nodes still need to decide which fragment to send when a contact happens. To address this problem, we propose EPICS, a protocol designed to quickly exchange large contents in opportunistic networks. Using grey relational analysis, EPICS is able to balance the distribution of contents that have different sizes and creation times, providing fairer delay distribution and faster dissemination. We implemented and evaluated EPICS through real experimentation using Android devices. Results show that EPICS significantly reduces content dissemination delays when compared to classic approaches.

One of the most engaging challenges for mobile operators today is how to manage the exponential data traffic increase. Mobile data offloading stands out as a promising and low cost solution to reduce the burden on the cellular network. To make this possible, we need a new hybrid network paradigm that leverages the existence of multiple alternative communication channels. This entails significant modifications in the way data is handled, affecting also the behavior of network protocols. In this paper, we present a comprehensive survey of data offloading techniques in cellular networks and extract the main requirements needed to integrate data offloading capabilities into today’s mobile networks. We classify existing strategies into two main categories, according to their requirements in terms of content delivery guarantees: delayed and non-delayed offloading. We overview the technical aspects and discuss the state of the art in each category. Finally, we describe in detail the novel functionalities needed to implement mobile data offloading in the access network, as well as current and future research challenges in the field, with an eye toward the design of hybrid architectures.

Cellular networks face significant challenges as a result of the growth of cellular data traffic, with a significant portion of such traffic being delay tolerant. Multi-hop Cellular Networks (MCNs) can help address the foreseen capacity and energy-efficiency constraints of cellular networks through the integration of cellular and Device to Device (D2D) communications. To this aim, this work proposes and studies the use of opportunistic store, carry and forward mechanisms in MCN networks to increase energy efficiency for delay tolerant traffic. Numerical and simulation results demonstrate that significant energy benefits (above 90%) can be achieved through the use of opportunistic forwarding in MCN networks.

In a context where networks grow larger and larger, their nodes become more likely to fail. Indeed, they may be subject to crashes, attacks, memory corruptions… To encompass all possible types of failure, we consider the most general model of failure: the Byzantine model, where any failing node may exhibit arbitrary (and potentially malicious) behavior. We consider an asynchronous grid-shaped network where each node has a probability delta to be Byzantine. Our metric is the communication probability, that is, the probability that any two nodes communicate reliably. A number of Byzantine-resilient broadcast protocols exist, but they all share the same weakness: when the size of the grid increases, the communication probability approaches zero.
In this paper, we present the first protocol that overcomes this difficulty, and ensures a communication probability of 1-4delta on a grid that may be as large as we want (for a sufficiently small delta, typically delta < 10^-5). The originality of the approach lies in the fractal definition of the protocol, which, we believe, could be used to solve several similar problems related to scalability. We also extend this scheme to a 3-dimensional grid and obtain a 1-2delta communication probability for delta < 10^-3.

Most existing proposals in the area of disruption-tolerant networking rely on the binary assertion that when two nodes are not in contact, they are necessarily in intercontact. Such a monolithic notion is, in our opinion, too limitative. In this paper, we advocate the use of the neighborhood of a node beyond one hop to help design more efficient communication solutions. We provide a formal definition of κ-vicinity and associated measures, namely κ-contact and κ-intercontact . These measures allow better understanding the proximity between nodes as they are not restrained solely to the direct contact situation. We observe unexpected behaviors in κ-contact distributions and point out their dependency on node density. We also observe that a significant share of pairs of nodes spend a non-negligible amount of time in each other’s vicinity without coming into direct contact. We show then that using a small κ (between 2 and 4) is enough to capture a significant amount of communication possibilities that are neglected by existing approaches. Finally, we provide a rule of thumb to derive the population in the κ-vicinity by observing only the direct contacts of a node.

A natural method to disseminate popular data on cellular networks is to use multicast. Despite having clear advantages over unicast, multicast does not offer any kind of reliability and could result costly in terms of cellular resources in the case at least one of the destinations is at the edge of the cell (i.e., with poor radio conditions). In this paper, we show that, when content dissemination tolerates some delay, providing device-to-device communications over an orthogonal channel increases the efficiency of multicast, concurring also to offload part of the traffic from the infrastructure. Our evaluation simulates an LTE macro-cell with mobile receivers and reveals that the joint utilization of device-to-device communications and multicasting brings significant resource savings while increasing the cellular throughput.

Cellular operators count on the potential of offloading techniques to relieve their overloaded radio access networks. In this paper, we propose, design, and evaluate a re-injection strategy to finely control the opportunistic distribution of popular contents throughout a hybrid mobile network. The idea is to use the infrastructure resources as seldom as possible. Unlike existing techniques that bind re-injection to statically defined objective functions, our proposal adapts to the current network topology. This turns out to be particularly effective in highly dynamic scenario, where clustering prevent contents to diffuse properly. We assess the performance of our strategy by re-running a realistic large-scale (more than 10,000 nodes) vehicular dataset to disseminate contents under different tolerances to delay. The results show significant savings in the infrastructure load between 55% and 63%.

Cellular operators count on the potentials of offloading techniques to relieve their overloaded data channels. Beyond standard access point-based offloading strategies, a promising alternative is to exploit opportunistic direct communication links between mobile devices. Nevertheless, achieving efficient device-to-device offloading is challenging, as communication opportunities are, by nature, dependent on individual mobility patterns. We propose, design, and evaluate DROiD (Derivative Re-injection to Offload Data), an original method to finely control the distribution of popular contents throughout a mobile network. The idea is to use the infrastructure resources as seldom as possible. To this end, DROiD injects copies through the infrastructure only when needed: (i) at the beginning, in order to trigger the dissemination, (ii) if the evolution of the opportunistic dissemination is below some expected pace, and (iii) when the delivery delay is about to expire, in order to guarantee 100% diffusion. Our strategy is particularly effective in highly dynamic scenarios, where sudden creation and dissolution of clusters of mobile nodes prevent contents to diffuse properly. We assess the performance of DROiD by simulating a traffic information service on a realistic large-scale vehicular dataset composed of more than 10,000 nodes. DROiD substantially outperforms other offloading strategies, saving more than 50% of the infrastructure traffic even in the case of tight delivery delay constraints. DROiD allows terminal-to-terminal offloading of data with very short maximum reception delay, in the order of minutes, which is a realistic bound for cellular user acceptance.

When studying and designing protocols for mobile opportunistic networks, most works consider only direct contact patterns between mobile nodes. Tracking these contacts is important for end-to-end communications but relying only on this kind of information provides a limited view about transmission possibilities. Mobile users are often in intercontact, but still separated by only a few hops, which translate into effective communication opportunities between nodes. In this paper, we focus on such a type of communication opportunities and investigate to what extent they can be predicted. Using realworld datasets, we provide evidences about the predictable nature of nodes’ proximity and evaluate the benefits of these results compared to direct contact predictions.

Modeling the dynamics of opportunistic networks relies on the dual notion of contacts and intercontacts. We propose the use of an extended view in which nodes track their extended vicinity (up to a few hops) and not only their direct neighbors. We introduce a method that allows nodes predicting whether other nodes will be within reach given their current position and previous movements. This approach is contrary to existing ones where contact patterns are derived from the spatial mobility of nodes. We apply our method to several real-world and synthetic datasets. Firstly, we provide a novel algorithm and an intuitive modeling to understand a node’s surroundings. Then, we highlight two main behaviors of vicinity chains. Finally, three main types of movements (birth, death, and sequential) are identified as well as their predominant patterns. These whole analysis culminates in the development of a neighborhood generator capable of generate intercontact traces betweem pairs of nodes. Such generator can create traces preserving real characteristics for simulations in different time scales.

Modeling the dynamics of opportunistic networks generally relies on the dual notion of contacts and intercontacts between nodes. We advocate the use of an extended view in which nodes track their vicinity (within a few hops) and not only their direct neighbors. Contrary to existing approaches in the literature in which contact patterns are derived from the spatial mobility of nodes, we directly address the topological properties avoiding any intermediate steps. To the best of our knowledge, this paper presents the first study to ever focus on vicinity motion. We apply our method to several real-world and synthetic datasets to extract interesting patterns of vicinity. We provide an original workflow and intuitive modeling to understand a node’s surroundings. Then, we highlight two main vicinity chains behaviors representing all the datasets we observed. Finally, we identify three main types of movements (birth, death, and sequential). These patterns represent up to 87% of all observed vicinity movements.

Collecting real contact traces in disruption-tolerant networks is a complex procedure. From the experiment setup to the data cleaning, a lot of efforts is required. We propose a breeding system to derive possible contact traces from a single real experiment. We check the consistency of our system using synthetic mobility traces and show that bred traces do follow characteristics of the original trace.We apply then the system to a real-world contact dataset and derive different traces by varying both the probing interval and the transmission range. Our results indicates that: (i) even with high measurement frequencies, our system produces accurate traces, and (ii) by breeding a real trace, we are able to extract valuable network observations that would have been possible only with a new experimental campaign.

Most disruption-tolerant networking protocols available have focused on mere contact and intercontact characteristics to make forwarding decisions. We propose to relax such a simplistic approach and include multi-hop opportunities by annexing a node’s vicinity to its network vision. We investigate how the vicinity of a node evolves through time and whether such information is useful when routing data. By analyzing a modified version of the pure WAIT forwarding strategy, we observe a clear tradeoff between routing performance and cost for monitoring the neighborhood. By observing a vicinity-aware WAIT strategy, we emphasize how the pure WAIT misses interesting end-to-end transmission opportunities through nearby nodes. Our analyses also suggest that limiting a node’s neighborhood view to four hops is enough to improve forwarding efficiency while keeping control overhead low.

Modeling human mobility is crucial in the analysis and simulation of opportunistic networks, where contacts are exploited as opportunities for peer-to-peer message forwarding. The current approach to human mobility modeling has been based on continuously modifying models, trying to embed in them the mobility properties (e.g., visiting patterns to locations or specific distributions of inter-contact times) as they arose from trace analysis. As a consequence, with these models it is difficult, if not impossible, to modify the features of mobility or to control the exact shape of mobility metrics (e.g., modifying the distribution of inter-contact times). For these reasons, in this paper we propose a mobility framework rather than a mobility model, with the explicit goal of providing a flexible and controllable tool for modeling mathematically and generating simulatively different possible features of human mobility.Our framework, named SPoT, is able to incorporate the three dimensions–spatial, social, and temporal–of human mobility. The way SPoT does this is by mapping the different social communities of the network into different locations, whose members visit with a configurable temporal pattern. In order to characterize the temporal patterns of user visits to locations and the relative positioning of locations based on their shared users, we analyze the traces of real user movements extracted from three location-based online social networks (Gowalla, Foursquare, and Altergeo). We observe that a Bernoulli process effectively approximates user visits to locations in the majority of cases, and that locations that share many common users visiting them frequently tend to be located close to each other. In addition, we use these traces to test the flexibility of the framework, and we show that SPoT is able to accurately reproduce the mobility behavior observed in traces. Finally, relying on the Bernoulli assumption for arrival processes, we provide a thorough mathematical analysis of the controllability of the framework, deriving the conditions under which heavy-tailed and exponentially-tailed aggregate inter-contact times (often observed in real traces) emerge.

Portable mobile devices like smartphones and tablets are the enablers for communications in mobile ad hoc networks. In order to optimise their energy usage, one of the most popular techniques is to implement a duty cycling policy, which periodically puts the user device in a energy saving mode (e.g., Bluetooth inquiry scan phase or turning off the WiFi interface) for a certain amount of time. Clearly, this strategy increases the battery lifetime, but it also has the net effect of reducing the number of usable contacts for delivering messages, increasing intercontact times and delays. In order to understand the effect of duty cycling in opportunistic networks, in this paper we propose a general model for deriving the pairwise intercontact times modified by a duty cycling policy. Then, we specialise this model when the original intercontact times are exponential (an assumption popular in the literature), and we show that, in this case, the intercontact times measured after duty cycling are, approximately, again exponential, but with a rate proportional to the inverse of the duty cycle. Once we have the distribution of the intercontact times after duty cycling, we use it for analysing how duty cycling affects the delay of message forwarding and the network lifetime.

Mobility-assisted networking is becoming very popular as a mean of delivering messages in disconnected or very dynamic networks, such as opportunistic networks. Despite the rapid growth in the number of proposals for routing protocols that exploit the mobility of nodes, there is a lack of general theoretical frameworks to be used for studying analytically their
performance under different mobility conditions (e.g., exponential or Pareto inter-meeting times). Moreover, one of the main approaches to forwarding (so-called utility-based forwarding) consists in nodes collecting statistics about their behaviour (e.g., their contact patterns), and using this information to guide the forwarding process. Thus, a general theoretical framework should also be able to model the fact that the statistics collected by nodes and used to make forwarding decisions might suffer from estimation errors. In order to fill these gaps, in this paper we propose an analytical framework for the single-copy forwarding process in a mobility-assisted network that has the following characteristics: (i) it provides a closed form solution for a large class of probability distributions representing intermeeting times, (ii) it is able to model both randomized and utility-based forwarding protocols, and (iii) it accounts for errors in the estimations of the utility values used by utility-based schemes for making forwarding decisions. We show that the framework is quite accurate and that it can be used to identify the most effective forwarding policies depending on the amount of estimation errors in the forwarding statistics.

The intermeeting time, i.e., the time between two consecutive contacts between a pair of nodes, plays a fundamental role in the delay of messages in opportunistic networks. A desirable property of message delay is that its expectation is ﬁnite, so that
the performance of the system can be predicted. Unfortunately, when intermeeting times feature a Pareto distribution, this property does not always hold. In this paper, assuming heterogeneous mobility and Pareto intermeeting times, we provide a detailed analysis of the conditions for the expectation of message delay to be ﬁnite (i.e., to converge) when social-oblivious or social-aware forwarding schemes are used. More speciﬁcally, we consider different classes of social-oblivious and social-aware schemes, based on the number of hops allowed and the number of copies generated. Our main ﬁnding is that, in terms of convergence, allowing more than two hops may provide advantages only in the social-aware case. At the same time, we show that using a multi-copy scheme can in general improve the convergence of the expected delay. Finally, we compare social-oblivious and social-aware strategies from the convergence standpoint and we prove that, depending on the mobility scenario considered, social-aware schemes may achieve convergence while social-oblivious cannot, and vice versa.

We propose Pavibe, a tool that analyzes real-world contact traces and generates vicinity traces for the evaluation of intermittentlyconnected mobile networks. The vicinity of a node is a useful parameter as it goes beyond the characterization of contacts and inter contacts by integrating nodes that can be reached within a few hops. Pavibe is composed of two complementary modules, namely vicinity motion and vicinity generator. The vicinity motion module parses real-world mobility traces to derive a state diagram that
captures the vicinity of a node. Based on this state diagram, the vicinity generator module is in charge of producing synthetic vicinity traces that are strongly inspired from the real-world, original trace. In this paper, we apply Pavibe on several scenarios and show that it properly works at multiple time scales. More than just relying on our observations concerning the datasets evaluated, we also deliver Pavibe to the research community to help this latter reduce its dependency to the very few available traces in the
literature.

Collecting real contact traces in disruption-tolerant networks is a complex procedure. From the experiment setup to the data cleaning, a lot of eorts is required. We propose a breeding system to derive possible contact traces from a single real experiment. We check the consistency of our system using synthetic
and trace-based mobility traces. The results show that bred traces do follow characteristics of the original trace. We apply then the system to dierent real-world traces and derive several traces by varying both the probing interval and the transmission range. Our results indicates that: (i) even with high measurement frequencies, our system produces accurate traces, and (ii) by breeding a real trace, we are able to extract valuable network observations that would have been possible only with a new experimental campaign.

Most existing proposals in the area of disruption-tolerant networking rely on the binary assertion that when two nodes are not in contact, they are necessarily in intercontact. Such a monolithic notion is, in our opinion, too limitative. In this paper, we advocate the use of the neighborhood of a node beyond one hop to help design more ecient communication solutions. We provide a formal definition of -vicinity and associated measures, namely -contact and -intercontact. These measures allow better understanding
the proximity between nodes as they are not restrained solely to the direct contact situation. We observe unexpected behaviors in contact distributions and point out their dependency on node density. We also observe that a significant share of pairs of nodes spend a non-negligible amount of time in each other’s vicinity without coming into direct contact. We show then that using a small (between 2 and 4) is enough to capture a significant amount of communication possibilities that are neglected by existing approaches. Finally, we provide a rule of thumb to derive the population in the -vicinity by observing only the direct contacts of a node.

Cellular operators count on the potentials of offloading techniques to relieve their overloaded data channels. Beyond standard access point-based offloading strategies, a promising alternative is to exploit opportunistic direct communication links between mobile devices. Nevertheless, achieving efficient device-to-device offloading is challenging, as communication opportunities are, by nature, dependent on individual mobility patterns. We propose, design, and evaluate DROiD (Derivative Re-injection to Offload Data), an original method to finely control the distribution of popular contents throughout a mobile network. The idea is to use the infrastructure resources as seldom as possible. To this end, DROiD injects copies through the infrastructure only when needed: (i) at the beginning, in order to trigger the dissemination, (ii) if the evolution of the opportunistic dissemination is below some expected pace, and (iii) when the delivery delay is about to expire, in order to guarantee 100% diffusion. Our strategy is
particularly effective in highly dynamic scenarios, where sudden creation and dissolution of clusters of mobile nodes prevent contents to diffuse properly.We assess the performance of DROiD by simulating a traffic information service on a realistic largescale vehicular dataset composed of more than 10,000 nodes. DROiD substantially outperforms other offloading strategies, saving more than 50% of the infrastructure traffic even in the case of tight delivery delay constraints. DROiD allows terminal-to-
terminal offloading of data with very short maximum reception delay, in the order of minutes, which is a realistic bound for cellular user acceptance.

Portable mobile devices like smartphones and tablets are the enablers for communications in mobile ad hoc networks. In order to optimise their energy usage, one of the most popular techniques is to implement a duty cycling policy, which periodically puts the user device in a energy saving mode (e.g., Bluetooth inquiry scan phase or turning off the WiFi interface) for a certain amount of time. Clearly, this strategy increases the battery lifetime, but it also has the net effect of reducing the number of usable contacts for delivering messages, increasing intercontact times and delays. In order to understand the effect of duty cycling in opportunistic networks, in this paper we propose a general model for deriving the pairwise intercontact times modified by a duty cycling policy. Then, we specialise this model when the original intercontact times are exponential (an assumption popular in the literature), and we show that, in this case, the intercontact times measured after duty cycling are, approximately, again exponential, but with a rate proportional to the inverse of the duty cycle. Once we have the distribution of the intercontact times after duty cycling, we use it for analysing how duty cycling affects the delay of message forwarding and the network lifetime.